| 作者单位 | College of Economics and Management, Northwest A&F University, Yangling, 712100, China Guanghua School of Management, Peking University, Beijing, 100871, China Institute for Artificial Intelligence, Peking University, Beijing, 100871, China College of Economics, Sichuan University, Chengdu, 610065, China Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China Center for Forecasting Science, Chinese Academy of Sciences, Beijing, 100190, China School of Economics and Management, University of Chinese Academy of Sciences, Beijing, 100190, China College of Economics, Sichuan Agricultural University, Chengdu, 611130, China |
| 摘要 | Using the survey data collected from rural households in Ningxia, Chongqing, and Sichuan provinces, this paper has identified the credit risk and measured the risk loss, under the context of land property rights controlled and the imperfect ecology of rural finance market in China. This paper uses machine learning method to identify farmers’ credit risk and verifies the effectiveness of this method compared with the traditional model. Also, Credit Risk+ model is employed to evaluate farmers’ credit risk. According to the survey statistics, the default rate of farmers’ farmland management right mortgages is relatively high, and it was 10%. Results show that the random forest model could identify the key factors of credit risk and predict the default probability effectively. Moreover, the expected loss and risk exposure of each loan is relatively high, and the risk loss increases rapidly under the impact of extreme events. In addition, it is helpful for financial institutions to optimize the financial capital structure and improve the risk management strategy to increase the investigation of farmers’ passive default motivation under the prior risk management framework. Thus, we conclude with several policy implications such as the accelerating development of fintech, improvement of rural credit investigation system, and innovation of risk pre-warning tools. ?? 2025 Systems Engineering Society of China. All rights reserved. |
